65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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tags:
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- text-generation
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- conversational
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- voice
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- insurance
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- qwen2.5
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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---
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# Insurance Real-Time Voice Bot (Qwen2.5-1.5B)
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Fine-tuned **Qwen2.5-1.5B-Instruct** for low-latency insurance sales voice replies:
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short spoken answers, one discovery question at a time.
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## Files
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| File | Use |
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|------|-----|
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| `model.safetensors` + tokenizer | Hugging Face Transformers |
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| `insurance-qwen25-1_5b-q4_k_m.gguf` | llama.cpp / low-latency GPU serve |
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## System prompt (recommended)
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```
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You are Aria, a real-time voice insurance sales agent. Speak in 1-2 short sentences. No lists or markdown. Ask exactly one discovery question when needed. Never invent prices or coverage. Never pressure or guarantee approval. Follow: greet → need (life/health/auto/home/business) → situation → next step (quote/compare/callback). If they want a human, offer transfer and ask when to call.
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```
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## Quick start (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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repo = "MUNEEBMN123/insurance-voice-qwen25-1_5b"
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tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
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)
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messages = [
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{"role": "system", "content": "You are Aria, a real-time voice insurance sales agent. Speak in 1-2 short sentences. Ask exactly one discovery question when needed."},
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{"role": "user", "content": "I need car insurance."},
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]
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text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tok(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=48, do_sample=False)
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print(tok.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
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```
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## Quick start (llama.cpp / GGUF)
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```bash
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huggingface-cli download MUNEEBMN123/insurance-voice-qwen25-1_5b insurance-qwen25-1_5b-q4_k_m.gguf --local-dir .
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./llama-server -m insurance-qwen25-1_5b-q4_k_m.gguf -ngl 99 -c 512 --port 8080
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```
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## Notes
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- Trained with QLoRA for concise insurance sales dialogue.
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- Prefer GGUF Q4 on a dedicated GPU (e.g. RTX 4090) for minimum latency.
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